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Application of deep learning in analysing morphological parameters of cervical computed tomography scans

  • Original Article
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Chinese Journal of Academic Radiology Aims and scope Submit manuscript

Abstract

Objectives

The process of measuring morphological parameters of the cervical spine through computed tomography (CT) is repetitive and time-consuming. Deep learning (DL) improves efficiency and consistency. In this study, we built a series of DL-based segmentation algorithms to automatically measure five key parameters for the evaluation of cervical spondylotic myelopathy. Subsequently, we compared the performance of our algorithm with that of physicians to assess its accuracy and clinical application value.

Methods

Cervical spine CT images of 685 patients were divided into a training (n = 548) and a test set (n = 137). The training set was used to develop a VB-Net DL model, including a 3D segmentation model of multiple cervical spine subregions and a key point location model on the midsagittal slice of the cervical spine CT. The parameters measured included sagittal vertebral canal diameter (SCD), sagittal vertebral body diameter (VBD), Pavlov’s ratio, transverse vertebral canal diameter (TCD), and osseous spinal canal area (OSCA). Manual measurements were performed by a radiologist and a spinal surgeon. The model’s performance was evaluated using the Mann–Whitney U test, Pearson correlation, mean absolute error, and Bland–Altman plots.

Results

DL and manual measurements of the Pavlov’s ratio, SCD, VBD, TCD, and OSCA in the test set showed similar accuracy and consistency. The VB-Net model’s Pearson correlation coefficient exceeded 0.8 for most parameters.

Conclusions

Our VB-Net-based DL approach can effectively approximate the manual measurements of cervical CT morphological parameters in humans, offing physicians an accurate and efficient auxiliary diagnostic tool.

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Data availability

The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

DL:

Deep learning

CSS:

Cervical spinal stenosis

CSM:

Cervical spondylotic myelopathy

DCS:

Developmental cervical stenosis

SCD:

Sagittal canal diameter

TCD:

Transverse canal diameter

VBD:

Vertebral body diameter

OSCA:

Osseous spinal canal area

OPLL:

Ossification of the posterior longitudinal ligament

References

  1. Ailon T, Smith JS, Shaffrey CI, et al. Outcomes of operative treatment for adult cervical deformity: a prospective multicenter assessment with 1-year follow-up. Neurosurgery. 2018;83:1031–9. https://doi.org/10.1093/neuros/nyx574.

    Article  PubMed  Google Scholar 

  2. Toki S, Higashino K, Manabe H, et al (2021) Morphometric analysis of subaxial cervical spine with myelopathy: a comparison with the normal population. Spine Surg Relat Res 5:34–40.https://doi.org/10.22603/ssrr.2020-0061

  3. Pavlov H, Torg JS, Robie B, Jahre C. Cervical spinal stenosis: determination with vertebral body ratio method. Radiology. 1987;164:771–5. https://doi.org/10.1148/radiology.164.3.3615879.

    Article  CAS  PubMed  Google Scholar 

  4. Aebli N, Wicki AG, Rüegg TB, et al. The Torg-Pavlov ratio for the prediction of acute spinal cord injury after a minor trauma to the cervical spine. Spine J. 2013;13:605–12. https://doi.org/10.1016/j.spinee.2012.10.039.

    Article  PubMed  Google Scholar 

  5. Kumar A, Sahu S, Sethi S, et al. Computerized tomography-based morphometric analysis of cervical spinal canal in central indian population. J Neurosci Rural Pract. 2020;11:274–7. https://doi.org/10.1055/s-0040-1703962.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Song K-J, Choi B-W, Kim S-J, et al. The relationship between spinal stenosis and neurological outcome in traumatic cervical spine injury: an analysis using Pavlov’s ratio, spinal cord area, and spinal canal area. Clin Orthop Surg. 2009;1:11–8. https://doi.org/10.4055/cios.2009.1.1.11.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chen X, Wang X, Zhang K, et al. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022;79: 102444. https://doi.org/10.1016/j.media.2022.102444.

    Article  PubMed  PubMed Central  Google Scholar 

  8. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44. https://doi.org/10.1038/nature14539.

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Zheng Q, Shellikeri S, Huang H, et al. Deep learning measurement of leg length discrepancy in children based on radiographs. Radiology. 2020;296:152–8. https://doi.org/10.1148/radiol.2020192003.

    Article  PubMed  Google Scholar 

  10. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39:1137–49. https://doi.org/10.1109/TPAMI.2016.2577031.

    Article  PubMed  Google Scholar 

  11. von Schacky CE, Wilhelm NJ, Schäfer VS, et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology. 2021;301:398–406. https://doi.org/10.1148/radiol.2021204531.

    Article  Google Scholar 

  12. Choi H, Kim YK, Yoon EJ, et al. Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer’s disease to Parkinson’s disease. Eur J Nucl Med Mol Imaging. 2020;47:403–12. https://doi.org/10.1007/s00259-019-04538-7.

    Article  PubMed  Google Scholar 

  13. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:640–51. https://doi.org/10.1109/TPAMI.2016.2572683.

    Article  PubMed  Google Scholar 

  14. Tajbakhsh N, Jeyaseelan L, Li Q, et al. Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal. 2020;63: 101693. https://doi.org/10.1016/j.media.2020.101693.

    Article  PubMed  Google Scholar 

  15. Balakrishnan G, Zhao A, Sabuncu MR, et al. VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging. 2019. https://doi.org/10.1109/TMI.2019.2897538.

    Article  PubMed  Google Scholar 

  16. Zou J, Gao B, Song Y, Qin J. A review of deep learning-based deformable medical image registration. Front Oncol. 2022;12:1047215. https://doi.org/10.3389/fonc.2022.1047215.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhao S, Wu X, Chen B, Li S. Automatic spondylolisthesis grading from MRIs across modalities using faster adversarial recognition network. Med Image Anal. 2019;58: 101533. https://doi.org/10.1016/j.media.2019.101533.

    Article  PubMed  Google Scholar 

  18. Pang S, Su Z, Leung S, et al. Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization. Med Image Anal. 2019;55:103–15. https://doi.org/10.1016/j.media.2019.04.012.

    Article  PubMed  Google Scholar 

  19. Alukaev D, Kiselev S, Mustafaev T, et al. A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation. Eur Spine J. 2022;31:2115–24. https://doi.org/10.1007/s00586-022-07245-4.

    Article  PubMed  Google Scholar 

  20. Nai R, Liu X, et al. Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach. Eur Radiol. 2023;33:566–77. https://doi.org/10.1007/s00330-022-08967-1.

    Article  PubMed  Google Scholar 

  21. Zhang S-C, Sun J, Liu C-B, et al. Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip. Bone Joint J. 2020;102B:1574–81. https://doi.org/10.1302/0301-620X.102B11.BJJ-2020-0712.R2.

    Article  Google Scholar 

  22. Kim Y-T, Jeong TS, Kim YJ, et al. Automatic spine segmentation and parameter measurement for radiological analysis of whole-spine lateral radiographs using deep learning and computer vision. J Digit Imaging. 2023. https://doi.org/10.1007/s10278-023-00830-z.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lee HM, Kim YJ, Cho JB, et al. Computer-aided diagnosis for determining sagittal spinal curvatures using deep learning and radiography. J Digit Imaging. 2022;35:846–59. https://doi.org/10.1007/s10278-022-00592-0.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yoo HJ, Kim YJ, Hong H, et al. Deep learning-based fully automated body composition analysis of thigh CT: comparison with DXA measurement. Eur Radiol. 2022;32:7601–11. https://doi.org/10.1007/s00330-022-08770-y.

    Article  CAS  PubMed  Google Scholar 

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Funding

This study received funding from Bei**g Natural Science Foundation (Z190020), National Natural Science Foundation of China (81971578, 81901791, 82102638), Peking University Third Hospital Clinical Key Project (BYSYZD2021040, BYSY2018003, BYSYZD2019005), Peking University Third Hospital Clinical Cohort Construction Project (BYSYDL2022007), Peking University Health Science Center Education and Teaching Research Project (2021YB05), Peking University Teaching and Learning 2.0 (2023YB01).

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Author notes

  1. Yuan Li, Enlong Zhang and Hanqiang Ouyang have contributed equally to this paper.

Authors and Affiliations

  1. Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Bei**g, 100191, People’s Republic of China

    Yuan Li, Enlong Zhang, Shuai Tian, Ning Lang & Huishu Yuan

  2. Department of Orthopaedics; Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Bei**g Key Laboratory of Spinal Disease Research, 49 North Garden Road, Haidian District, Bei**g, 100191, People’s Republic of China

    Hanqiang Ouyang, Huacheng Pang, Yating Hao, Daole Hu, Shuyu Dong, **angjun Shi & Liang Jiang

  3. Department of Research and Development, United Imaging Intelligence (Bei**g) Co.,Ltd, Yongteng North Road, Haidian District, Bei**g, 100089, People’s Republic of China

    **aoming Liu & Pei Dong

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  1. Yuan Li
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Contributions

All authors contributed extensively to the work presented in this paper. Their roles are given below. Conception and design: HQO, YL. Administrative support: NL, HSY. Provision of study materials or patients: HQO, ST, YL. Collection and assembly of data: ELZ, HCP, YTH, DLH, SYD. Data analysis and interpretation: XML,PD. Manuscript writing: all authors. Final approval of manuscript: all authors.

Corresponding authors

Correspondence to Ning Lang or Huishu Yuan.

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Conflicts of interest

The authors declare no conflict of interest.

Institutional review board statement

The methods were performed in accordance with relevant guidelines and regulations and approved by Peking University Third Hospital Medical Science Research Ethic Committee.

Informed consent statement

Patient consent was waived due to retrospective study.

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Li, Y., Zhang, E., Ouyang, H. et al. Application of deep learning in analysing morphological parameters of cervical computed tomography scans. Chin J Acad Radiol 7, 50–57 (2024). https://doi.org/10.1007/s42058-024-00136-1

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  • DOI: https://doi.org/10.1007/s42058-024-00136-1

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